Experimental and numerical spatio-temporal analysis of the Portevin–Le Chatelier dynamics in a Nickel based superalloy

Abstract

This study examines the Portevin–Le Chatelier (PLC) effect in the nickel-based superalloy Inconel 718 through a combined experimental-computational approach using statistical indicators from nonlinear dynamical systems theory. We develop a finite element model incorporating the Kubin–Estrin–McCormick constitutive law to capture Dynamic Strain Ageing effects, accounting for machine stiffness influence, and reproduce various dynamics under both hard and soft loading conditions. Statistical analysis reveals that machine stiffness significantly affects serration morphology and dynamics, influencing mean amplitudes, stress drop periods, and dynamical indicators such as the correlation dimension and Lyapunov exponents. Comparison of simulated and experimental stress time series demonstrates chaotic behaviour for type B and C serrations across all strain rates, with no evidence of self-organised criticality in experimental data. However, simulations predict self-organised criticality at high strain rates corresponding to type A bands, consistent with literature references. Statistical indicators reveal power-law behaviour for stress drop amplitudes as a function of strain rate, with critical exponents dependent on band type. Multifractal analysis shows that simulations overestimate complexity relative to experimental observations, suggesting the need for additional internal variables and finer-scale dynamics in modelling. Additionally, multifractal analysis of spatio-temporal diagrams reveals power-law distributions of plastic strain rates with consistent critical exponents across all strain rates, demonstrating its potential for characterising PLC spatio-temporal dynamics. Statistical, dynamical, and multifractal indicators show consistent correlations, collectively capturing transitions between serration regimes and serving as reliable quantitative metrics for characterising PLC dynamics. The analysis is finally applied to the spatio-temporal strain fields measured by digital image correlation. The results demonstrate the value of multi-indicator analysis for assessing the agreement between experiment and simulation and subsequently improving constitutive model parameter identification.